ML for Loop Gain Identification of DC/DC Converters
Name
Chu-ccchu-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
8.95 MB
Format
Adobe PDF
Checksum (MD5)
488777f527fb634d4b3502eb780ffecc
Author(s)
Chu, Cecelia
Advisor(s)
Perreault, David J.
Lu, Wenjie
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
Control loop identification is necessary for evaluating the stability of switched power supplies and is therefore an important step during design and verification. Analytical models of power supplies often yield inaccurate predictions of the loop gain; therefore, power engineers traditionally must conduct slow, invasive loop gain measurements on physical hardware. This thesis presents an alternate approach to loop gain identification in which a machine learning model infers the frequency-domain loop response from the quick and convenient time-domain measurement of a load step transient. We show that we can train a neural network to accurately infer the loop gain of a current-mode buck converter over a generalized set of configurations and illustrate the disruptive potential of such a model with example applications such as live Bode plot monitoring and automatic loop compensation.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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